Advanced medical image processing wizard

ABSTRACT

An automatic medical image processing system includes a series of operation stages, each automating specifying the image processing parameters for processing medical images. In response to an image processing indicator, a first medical image is automatically identified, including determining a first image operation and image processing parameters, without user intervention. The first image operation is performed on the first medical image based on the image processing parameters. A second medical image is generated and transmitted to the client device to be presented therein. The client device displays a message prompting the user whether the user is satisfied with the second medical image. In response to a user input from the client device indicating that the user is unsatisfied with the second medical image, one or more remedial options are presented to allow the user selecting a remedial action to reprocess the first medical image.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/882,162, filed Sep. 25, 2013, which is incorporated by referenceherein in its entirety.

FIELD OF THE INVENTION

Embodiments of the present invention relate generally to medical imageprocessing systems. More particularly, embodiments of the inventionrelate to medical image processing wizard.

BACKGROUND

A computerized axial tomography scan (commonly known as a CAT scan or aCT scan) is an x-ray procedure, which combines many x-ray images withthe aid of a computer to generate cross-sectional views of the internalorgans and structures of the body. In each of these views, the bodyimage is seen as an x-ray “slice” of the body. Typically, parallelslices are taken at different levels of the body, i.e., at differentaxial (z-axis) positions. This recorded image is called a tomogram, and“computerized axial tomography” refers to the recorded tomogram“sections” at different axial levels of the body. In multislice CT, atwo-dimensional (2D) array of detector elements replaces the lineararray of detectors used in conventional CT scanners. The 2D detectorarray permits the CT scanner to simultaneously obtain tomographic dataat different slice locations and greatly increases the speed of CT imageacquisition. Multislice CT facilitates a wide range of clinicalapplications, including three-dimensional (3D) imaging, with acapability for scanning large longitudinal volumes with high z-axisresolution.

Magnetic resonance imaging (MRI) is another method of obtaining imagesof the interior of objects, especially the human body. Morespecifically, MRI is a non-invasive, non-x-ray diagnostic techniqueemploying radio-frequency waves and intense magnetic fields to excitemolecules in the object under evaluation Like a CAT scan, MRI providescomputer-generated image “slices” of the body's internal tissues andorgans. As with CAT scans, MRI facilitates a wide range of clinicalapplications, including 3D imaging, and provides large amounts of databy scanning large volumes with high resolution.

These image data are typically analyzed using complex software systemscalled advanced medical image processing systems. Advanced medical imageprocessing software is currently complex and unapproachable to all butthe experienced and trained user. However, as medical image processingsoftware becomes more integrated with medicine and the electronic healthrecord, it is becoming increasingly important for other users, such asother physicians and even non-physicians and patients, to be at leastversant on these software systems.

As advanced medical imaging software becomes more sophisticated andcommon, simplifying the use of such software packages becomes moreimportant and more challenging. Traditionally, Radiologists have beenthe primary user of sophisticated medical imaging software, and haveundergone extensive training to be proficient on these softwareplatforms. Radiologists may spend a significant portion of their timeusing such software packages and become experienced users.

Because of the software's complexity, it is virtually impossible for alay person to figure out how to use advanced medical image processingsoftware. Also, it is very difficult for an untrained physician to dothe same. Attempts to do so by the inadequately trained may result inmisinterpretation of image data and even medical mistakes such asmisdiagnoses.

There is a need for a simple way for a minimally trained or untraineduser to use advanced medical imaging software effectively.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating integrated medical record and/orclinical software and advanced imaging processing system according toone embodiment.

FIG. 2 is a block diagram illustrating an image processing systemaccording to another embodiment of the invention.

FIG. 3 is a block diagram illustrating an example of an image processingsoftware application according to one embodiment of the invention.

FIG. 4 is a block diagram illustrating an example of an automatic imageprocessing module according to one embodiment of the invention.

FIG. 5 is a block diagram illustrating an example of processing rules ofan automatic image processing system according to one embodiment of theinvention.

FIG. 6 is a flow diagram illustrating a processing flow of an automaticimage processing system according to one embodiment of the invention.

FIGS. 7A-7D are screenshots illustrating examples of graphical userinterface of an automatic image processing system according certainembodiments of the invention.

FIGS. 8A-8K are screenshots illustrating examples of graphical userinterface of an automatic image processing system according certainembodiments of the invention.

FIGS. 9A-9F are screenshots illustrating examples of graphical userinterface of an automatic image processing system according certainembodiments of the invention.

FIG. 10 is a flow diagram illustrating a process performed by anautomatic image processing system according to one embodiment of theinvention.

FIGS. 11A and 11B are block diagrams illustrating a cloud-based imageprocessing system according to certain embodiments of the invention.

FIG. 12 is a block diagram of a data processing system, which may beused with one embodiment of the invention.

DETAILED DESCRIPTION

Various embodiments and aspects of the inventions will be described withreference to details discussed below, and the accompanying drawings willillustrate the various embodiments. The following description anddrawings are illustrative of the invention and are not to be construedas limiting the invention. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentinvention. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present inventions.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the invention. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, a software/hardware system is providedwhich simplifies or automates the use of an advanced medical imageprocessing system. In one embodiment, in addition to an advanced imageprocessing system, an automatic image processing system (also referredto as a simplified image processing system) is provided. This automaticimage processing system may be layered on top of, or integrated with, anexisting or new advanced medical image processing software system tosimplify or automate the use of the medical image processing system,which may be implemented in software, hardware, or a combination ofboth. The automatic image processing system may be implemented in a formof an image processing wizard. The wizard guides a user through anadvanced image processing process. The wizard automates as many steps aspossible, for example, using preferences, assumptions, and/or a set ofrules, to process image data, such that the user does not have to knowthe details of how to operate the advanced image processing tools. Thewizard also gives the user an opportunity to confirm or change theresults that were created automatically or otherwise. The wizard mayconsist of the presentation of intuitive user interfaces as well as easyto answer questions which help guide the user through the imageprocessing process.

According to one embodiment, image processing software includes anautomatic image processing system that provides a user friendlyinteractive graphical user interface. The automatic image processingsystem allows a user to interact with the image processing softwarebased on a set of easily understandable processing stages to performcertain major or common or popular image processing operations on animage, without having to fully understand specific steps and/or imageprocessing parameters for processing the image. The automatic imageprocessing system may interact with the user through a series ofquestions and receive user inputs as a part of answers from the user todetermine the user's intent. Based on the user interaction with theautomatic image processing system, as well as metadata associated withimage data, such as patient ID, medical procedure, body part, medicalcondition or other data/tags, one or more image processing operationsmay be determined and either recommended to the user or processedautomatically. If recommended to the user, the user can select one ormore of the recommended image processing operations for processing theimage. One or more image processing parameters associated with theselected image processing operations are automatically determined by theunderlying logic of the image processing software without userintervention and without having the user providing the same parameters.The image processing software may be hosted by an image processingserver (which may include a Web server). A user can access the imageprocessing software from a client device over a network (e.g.,Internet). For example, the user can access the image processingsoftware using a Web browser or alternatively, using a clientapplication running at the client device.

Based on the image processing parameters, according to one embodiment,one or more image processing commands are generated and transmitted fromthe automatic image processing system to an image processing engine forimage processing. The image processing engine may be communicativelycoupled to the automatic image processing system, locally integratedwithin the image processing server or remotely via a set of APIs over anetwork. In response to receiving the image processing commends, theimage processing engine processes the image based on the imageprocessing parameters and generates a new or updated image. The newimage may represent a different view of the same medical data associatedwith the original image. The new image is then transmitted from theimage processing engine back to the automatic image processing system,which in turn transmits the new image to the client device to bepresented to the user. In one embodiment, the automatic image processingsystem causes the client device to prompt the user whether the user issatisfied with the new image. If the user is unsatisfied with the newimage, the automatic image processing system may communicate with theclient device to interact with the user for more user inputs concerningthe new image and further adjust the image processing parameters and theimage processing operations may be iteratively performed. As a result, auser does not have to fully understanding how to utilize the advancedimage processing system, although the advanced image processing systemmay also be available for advanced users. It is understood that a “view”or “image view” or “image” may contain information in addition toimages, including quantitative data, identifying marks, measurements,outlines, mapping, layers such as color layers, etc. This additionaldata may be contained in image metadata. Also, “image” may mean one ormore images, for example, it could represent an image series from a CTscan.

In one embodiment, image processing client software (e.g., thin clientsoftware) may be integrated with medical record and/or clinical trial(MRCS) client software, where the integrated MRCS client software may beable to access both a medical record server and the image processingsoftware hosted by an image processing server to process medical imagesthat are associated with a medical record of a particular patient. Theautomatic image processing system allows a user to select and process amedical image that is associated with a medical record of a patient,such as, for example, an image associated with a body part, a medicalprocedure, a medical appointment with doctors, and/or a medicalcondition of a patient, etc.

FIG. 1 is a block diagram illustrating integrated MRCS and advancedimaging processing system according to one embodiment. Referring to FIG.1, according to one embodiment, system 100 includes a client 105communicatively coupled to a medical imaging processing server 110 andMRCS server 115 over a network 101, wired and/or wirelessly. Network 101may be a local area network (LAN), a metropolitan area network (MAN), awide area network (WAN) such as the Internet or an intranet, a privatecloud network, a public cloud network, or a combination thereof.Although FIG. 1 illustrates a single client 105 communicatively coupledto the imaging processing server 110 and medical data server 115, itwill be appreciated that multiple such clients may be communicativelycoupled to the medical imaging processing server 110 and/or MRCS server115.

MRCS server 115, in one embodiment, may be a data server that residesphysically in a different location from the medical imaging processingserver 110 and the client 105. In another embodiment, the MRCS server115 may be in the same geographic location as the medical imagingprocessing server 110 and/or client 105. MRCS server 115 may be operatedby the same or different organization from client 105 and/or imagingprocessing server 110. In one embodiment, the MRCS server 115 includesdata storage to store medical records of patients such as EMRs or EHRs102. MRCS server 115 may also store clinical trial records 103 ofanonymous patients. MRCS server 115 further includes access controlsystem 116 for providing access to EMR Data 102 and trial records 103.Different users having different roles may be allowed to accessdifferent data. For example, a doctor may be allowed to access EMR data102, while a medical student or professor may be allowed to access onlythe trial records 103. For the purpose of illustration, MRCS server 115may represent a MRS server, a CTS server, or a combination of both andMRCS sever 115 may be implemented in a single server or a cluster ofservers. Also note that MRCS server 115 may represent two separateserves: 1) a MRS server having EMR data 102 stored therein; and 2) a CTSserver having trial records 103 stored therein.

Medical imaging processing server 110 includes image processing engine104 which is configured to provide medical image processing services toclient 105 over a network. In one embodiment, the medical imagingprocessing server 110 also includes an image store 108 to store medicaldata such as digital imaging and communications in medicine (DICOM)compatible data or other image data, including JPEG, TIFF, video, EKG,laboratory images, reports, text, PDF, sound, and other files. The imagestore 108 may also incorporate encryption capabilities, where themedical data can be stored and transmitted in an encrypted form. Theimage store 108 may include multiple databases, and may be implementedwith relational database management systems (RDBMS), e.g., Oracle™database or Microsoft® SQL Server, etc.

In one embodiment, the medical imaging processing server 110 includes anaccess control system 106 to control access, by the client 105, ofresources (e.g., image processing tools) and/or medical data stored inimage store. Client 105 may or may not access certain portions or typesof resources and/or medical data stored in image store depending uponits access privilege. The access privileges may be determined orconfigured based on a set of role-based rules or policies. For example,client 105 may be configured with certain roles that only permit accessto some of the tools provided by the medical imaging processing server110. In other instances, client 105 may be configured with certain rolesthat limit its access to some patient information. For example, certainusers (e.g., doctors, medical students) of client 105 may have differentaccess privileges to access different medical information stored inimage store 108 or different imaging rendering resources provided byimaging processing server 110.

Client device 105 is a client which may include integrated medicalsoftware 107 as described herein. In one embodiment, the integratedsoftware 107 integrates image(s) and/or image processing functionality121 with medical record software (MRS) and/or clinical trial software(CTS) 107, which herein are collectively referred to as medical recordand/or clinical software (MRCS). For example, imaging processingfunction may be implemented as a medical imaging processing client 121communicatively coupled to image processing server 110 over network 101.Imaging processing client 121 may be linked to medical software 107 orembedded within medical software 107. MRCS client 107 and MedicalImaging Processing client may also be completely separate, i.e.non-integrated.

MRS is patient-centric software that focuses on medical records of theindividual patients. Patient-centric means here that the software'sprimary purpose is to record and view data relating to the individualpatient. This type of software may be referred to as electronic medicalrecord (EMR) software, electronic health record (EHR) software, personalhealth record (PHR) software and other names. Information maintained bythe MRS typically includes: patient ID, demographic, info—age, weight,height, Blood Pressure (BP), etc., lab orders and results, test ordersand results, medical history, appointment history, appointmentsscheduled, exam history, prescriptions/medications, symptoms/diagnoses,and insurance/reimbursement info.

CTS includes software for both retrospective and prospective clinicalstudies. This type of software may be referred to as a Clinical TrialManagement System. CTS may also include software for research. CTS istrial-centric which means the primary purpose of the software is tocollect and view aggregate data for multiple patients or participants.Although data is collected at the individual patient/participant level,this data is usually viewed “blindly”. This means that the viewer and/oranalyzer of the data generally do not know the identity of theindividual patients/participants. However, data can be viewed at theindividual patient/participant level where necessary. This isparticularly important where images are involved. CTS typicallyincludes: patient ID, concomitant medications, adverse events,randomization info, data collection, informed consent, aggregated data,and status of study.

In one embodiment, the MRCS 107 of the integrated medical softwareexecuted within the client 105 displays medical information 122 of apatient, including, e.g., the medical treatment history of a patient,which may be part of a medical record and/or trial record 120 of thepatient. Such records 120 may be downloaded from medical data server 115in response to a user request. In the case where the integrated medicalsoftware integrates MRS, the patient's full identity it typicallydisplayed as part of the medical information. On the other hand, in thecase of an integrated CTS, the patient is typically anonymous asdiscussed above, and the identity of the patient is typically notrevealed as part of the displayed medical information.

In one embodiment, image(s) and/or image processing function 121 isintegrated with the MRCS. Integration can take the form of the image(s)and/or image processing tools showing up in the same window as the MRCS.Integration can also take the form of a window containing the image(s)and/or image processing tools opening up in a separate window from theMRCS window. It should be noted, however, that in either form ofintegration, the medical information of the patient and image(s) aredisplayed within the integrated medical software, without requiring theuser of the integrated software to separately obtain the images viaanother software program.

In one embodiment, when the advanced image processing system isutilized, a set of graphical representation representing a set of imageprocessing tools may be presented in an advanced image processinggraphical user interface to allow a user to specify one or more of theimage processing tools to process a particular one of images 124. Whenthe automatic image processing system is utilized, the underlyingprocessing logic of automatic image processing system 150 is configuredto automatically determine and select one or more image processing toolsto process the image, for example, without user intervention or userknowledge of which of the image processing tools to be utilized. Thegraphical representations (e.g., icons) for image processing tools thatare provided by the remote imaging processing server 110 are displayedto the user of the integrated medical software executed on the client105. In such an embodiment, the available image processing tools aredisplayed in the integrated medical software as a set of icons or someother graphical representations, which when activated by a user, allowan image to be manipulated by remote imaging processing server 110. Inone embodiment the image processing software is integrated with the MRCSprogram and also opens up “in context”. “In context” means that theimage processing software opens up to show the appropriate image(s)and/or tools for the current user and/or patient and/or affliction. Theavailability of imaging tools to a particular user depends on the accessprivileges of that particular user (e.g., doctors vs. medical students).Alternatively, the availability of imaging tools may be determined basedon a particular body part of a patient, which may be identified bycertain tags such as DICOM tags.

For example, one doctor may prefer that the cardiovascular images forhis patients open up in a 3D view, with vessel centerline toolsavailable, yet the abdominal images for his patients open up in acoronal view with the flythrough, or virtual colonoscopy, toolsavailable. He may prefer to have the other views and tools hidden fromview. In another example, another doctor may prefer that the images forher patients open up showing the most recent views and tools that sheused for that patient. In another example, the default view forcardiovascular cases may be set to show a particular view and tools, butthe user may be able to change the default so that his/her preferencesoverride the default views and tools.

In all of the above examples, ideally only the images that relate to thepatient being evaluated at that time are able to be viewed. In addition,the user/physician does not need to search to find the images relatingto the patient, the images 124 are automatically associated with thecorrect patient, for example, based on the corresponding patient ID. Todo this, the identity of the patient needs to be associated with thepatient's images. This can be done by using tags, such as a commonidentifier, such as an ID number, metadata associated with one or moreof the images, mining patient data, body part analysis, or other ways.Also, the appropriate tools need to be shown and inappropriate toolshidden. The tags are discussed in more details below.

For example, an image or image series can be analyzed to determinewhether it is a head, abdomen, or other body part, based on the anatomy.A skull has a characteristic shape, as do other parts of the anatomy. Acatalog of reference images may be used to help identify specific bodyparts. Based on this analysis, the appropriate views and/or tools can bemade visible to the user, and inappropriate views and/or tools can behidden. For example, if the image series is of a head/skull, the imageseries may be shown in a certain view, such as an axial view, and toolsassociated with the brain visible. In addition, if certain key words,such as “tumor” or “stroke”, are found in the MRCS record, specifictools may be shown, such as tools that detect a tumor or evaluate brainperfusion. It is also possible that a patient ID can be determined fromthe anatomy in an image based on shape, disease, tags etc. For example,an image of a dental area can be matched with dental records to identifya patient from medical images. Or, an identifying tag can be included inthe medical image—such as a tag with the patient ID number placed on ornear the table of a CT scanner, or on the patient himself. In anotherembodiment, the user of the software is able to customize how the imageprocessing software is presented in context. For example, Doctor Y, acardiologist, may prefer to have the images open up in a 3D model view,and have cardiology tool A and cardiology tool B visible to him. In thisexample, other views may be hidden (for example, the axial, sagittal,and coronal views) and other tools are hidden (for example, toolsrelating to the colon or the brain).

According to one embodiment, image processing server 110 includesadvance image processing system 140 to allow users of different types toaccess the imaging tools represented by tool icons for processing images124, which utilize processing resources (e.g., image processing engine104) provided by medical image processing server 110 over network 101.Image processing server 110 also includes automatic image processingsystem 150 to allow users of different types to access the functionalityof imaging tools without having to deal with the tools directly. Thefunctionality of the tools is provided by medical image processingserver 110 over network 101. Automatic image processing system 150 maybe layered on top of, or integrated with, an existing or new advancedmedical image processing software system (e.g., advanced imageprocessing system 140) to simplify or automate the use of the medicalimage processing resources (e.g., image processing engine 104), whichmay be implemented in software, hardware, or a combination of both.

According to one embodiment, both advanced image processing system 140and automatic image process system 150 may access the image processingfunctions (e.g., libraries, routines, tools, etc.) of image processingengine 104 via a set of application programming interfaces (APIs) orcommunication protocols (if image processing engine 104 is a remotesystem over a network). When advanced image processing system 140 isutilized, according to one embodiment, an advanced graphical userinterface may be presented, for example, similar to the graphical userinterface as shown in FIG. 8H, to allow the user to specify detailedimage processing parameters for processing a specific image selected bythe user. The underlying processing logic of advanced image processingsystem 140 (e.g., advanced image processing module 303 of FIG. 3)processes the user inputs received from the advanced graphical userinterface and formulates one or more image processing commands with aset of image processing parameters that are generated based on the userinputs. The processing logic of advanced image processing system 140then sends the commands, for example, via the APIs, to image processingengine 104 to process the image.

When automatic image processing system 150 is utilized, according to oneembodiment, a simplified graphical user interface (e.g., wizard) ispresented at a client device of the user to walk the user through aseries of simple steps or interactive questions without requiring theuser to specify the detailed operational image processing parameters.The underlying processing logic (e.g., automatic image processing module304 of FIG. 3) is configured to automatically determine the detailedimage processing parameters based on the user interaction with thesimplified graphical user interface. A set of image processing commandsis generated and sent to image processing engine 104 for processing theimage. Alternatively, the underlying processing logic of automatic imageprocessing system 150 determines the parameters and passes theparameters to the advanced image processing system 140, just as theadvanced image processing system would have received from a user via itscorresponding graphical user interface. The advanced image processingsystem 140 in turn communicates with image processing engine 104 onbehalf of automatic image processing system 150.

The automatic image processing system 150 may be implemented in a formof an image processing wizard. The wizard guides a user through theadvanced image processing process. The wizard automates as many steps aspossible, for example, using preferences, assumptions, and a set ofrules, to process image data, such that the user does not have to knowthe details of how to operate the advanced image processing tools. Thewizard also gives the user an opportunity to confirm or change theresults that were created automatically or otherwise. The wizard mayconsist of the presentation of intuitive user interfaces as well as easyto answer questions which help guide the user through the imageprocessing process.

According to one embodiment, automatic image processing system 150provides a user friendly interactive graphical user interface. Theautomatic image processing system 150 allows a user to access theunderlying processing resources of image processing server 110 based ona set of easily understandable processing stages to perform certainmajor or common or popular image processing operations on an image,without having to fully understand specific steps and/or imageprocessing parameters or tools for processing the image. The automaticimage processing system 150, through a user friendly graphical userinterface (GUI), may interact with the user through a series ofquestions and receive user inputs as a part of answers from the user todetermine the user's intent. Based on the user interaction with theautomatic image processing system 150, one or more image processingoperations may be determined and recommended to the user via automaticimage processing system 150. The user can select one or more of therecommended image processing operations for processing the image, oralternatively, image processing operations may be performedautomatically by the automatic image processing system 150. Based on auser selection of one or more of the image processing indicators, one ormore image processing parameters associated with the selected imageprocessing operations are automatically determined without userintervention and without having the user providing the same parameters.

Based on the image processing parameters received by the automatic imageprocessing system 150, according to one embodiment, one or more imageprocessing commands are generated and transmitted from the automaticimage processing system 150 to image processing engine 104 for imageprocessing. In response to the image processing commends, imageprocessing engine 104 of image processing server 110 processes the imagebased on the image processing parameters and generates a new or updatedimage. The new image may represent a different view of the same medicaldata associated with the original image. The new image is thentransmitted from the image processing server 110 back to automatic imageprocessing system 150, which in turn transmits the new image to clientdevice 105 to be presented to the user. The automatic image processingsystem 150 also causes client 105 to prompt the user whether the user issatisfied with the new image. If the user is unsatisfied with the newimage, automatic image processing system 150 may interact with the userfor more user inputs concerning the new image and further adjust theimage processing parameters and the image processing operations may beiteratively performed. As a result, a user does not have to fullyunderstanding how to utilize the advanced image processing system,although the advanced image processing system may also be available foradvanced users.

FIG. 2 is a block diagram illustrating an image processing systemaccording to another embodiment of the invention. In this embodiment,image processing clients may be implemented as standalone clientsaccessing an image processing server over the cloud (e.g., Internet).Referring to FIG. 2, system 200 includes image processing server 110communicatively coupled to one or more clients 105A-105B over network101, which may be a LAN, MAN, WAN, or a combination thereof. Server 110is configured to provide cloud-based image processing services toclients 105A-105B based on a variety of usage licensing models. Each ofclients 105A-105B includes a client application such as clientapplications 211-212 to communicate with server 110, respectively, toaccess resources provided by server 110. Client applications 211-212 maybe thin client applications or browser applications. Server application209 may be implemented as a virtual server or instance of the serverapplication 110, one for each client.

According to one embodiment, server 110 includes, but is not limited to,workflow management system 205, medical data store 206, image processingsystem 104, and access control system 106. Medical data store 206 may beimplemented as part of database 110 of FIGS. 1A and 1B. Medical datastore 206 is utilized to store medical images and image data receivedfrom a medical data center (e.g., PACS systems, not shown) or otherimage storage systems 215 (e.g., CD-ROMs, or hard drives) and processedby image processing system 104 and/or image preprocessing systems 204.Image processing system 104 includes a variety of medical imagingprocessing tools or applications that can be invoked and utilized byclients 105A-105B via their respective client applications 211-212,respectively, according to a variety of licensing terms or agreements.It is possible that in some medical institutes that the image storagesystem 215 and an image capturing device may be combined.

In response to image data received from a medical data center or fromimage capturing devices (not shown) or from another image source, suchas a CD or computer desktop, according to one embodiment, imagepreprocessing system 204 may be configured to automatically performcertain preprocesses of the image data and store the preprocessed imagedata in medical data store 206. For example, upon receipt of an imagedata from PACS or directly from medical image capturing devices, imagepreprocessing system 204 may automatically perform certain operations,such as bone removal, centerline extraction, sphere finding,registration, parametric map calculation, reformatting, time-densityanalysis, segmentation of structures, and auto-3D operations, and otheroperations, some of which are listed later herein. Image preprocessingsystem 204 may be implemented as a separate server or alternatively, itmay be integrated with server 110. Furthermore, image preprocessingsystem 204 may perform image data preprocesses for multiple cloudservers such as server 110.

In one embodiment, a client/server image data processing architecture isinstalled on system 200. The architecture includes client partition(e.g., client applications 105A-105B) and server partition (e.g., serverapplications 209). The server partition of system 200 runs on the server110, and communicates with its client partition installed on clients105A-105B, respectively. In one embodiment, server 110 is distributedand running on multiple servers. In another embodiment, the system is aWeb-enabled application operating on one or more servers. Any computeror device with Web-browsing application installed may access and utilizethe resources of the system without any, or with minimal, additionalhardware and/or software requirements.

In one embodiment, server 110 may operate as a data server for medicalimage data received from medical image capturing devices. The receivedmedical image data is then stored into medical data store 206. In oneembodiment, for example, when client 105A requests for unprocessedmedical image data, server application 110 retrieves the data from themedical data store 206 and renders the retrieved data on behalf ofclient 105A.

Image preprocessing system 204 may further generate workflow informationto be used by workflow management system 205. Workflow management system205 may be a separate server or integrated with server 110. Workflowmanagement system 205 performs multiple functions according to someembodiments of the invention. For example, workflow management system205 performs a data server function in acquiring and storing medicalimage data received from the medical image capturing devices. It mayalso act as a graphic engine or invoke image processing system 207 inprocessing the medical image data to generate 2D or 3D medical imageviews.

In one embodiment, workflow management system 205 invokes imageprocessing system 104 having a graphics engine to perform 2D and 3Dimage generating. When a client (e.g., clients 105A-105B) requests forcertain medical image views, workflow management system 205 retrievesmedical image data stored in medical data store 206, and renders 2D or3D medical image views from the medical image data. The end results formedical image views are sent to the client.

In one embodiment, a user makes adjustments to the medical image viewsreceived from server 110, and these user adjustment requests are sentback to the workflow management system 205. Workflow management system205 then performs additional graphic processing based on the userrequests, and the newly generated, updated medical image views arereturned to the client.

As described above, when implemented as a cloud based application,system 200 includes a client-side partition and a server-side partition.Functionalities of system 200 are distributed to the client-side orserver-side partitions. When a substantial amount of functionalities aredistributed to the client-side partition, system 200 may be referred toas a “thick client” application. Alternatively, when a limited amount offunctionalities are distributed to the client-side partition, while themajority of functionalities are performed by the server-side partition,system 200 may be referred to as a “thin client” application. In anotherembodiment, functionalities of system 200 may be redundantly distributedboth in client-side and server-side partitions. Functionalities mayinclude processing and data. Server 110 may be implemented as a webserver. The web server may be a third-party web server (e.g., Apache™HTTP Server, Microsoft® Internet Information Server and/or Services,etc.) The client applications 211-212 may be a web browser.

In one embodiment, workflow management system 205 manages the creation,update and deletion of workflow templates. It also performs workflowscene creation when receiving user requests to apply a workflow templateto medical image data. A workflow is defined to capture the repetitivepattern of activities in the process of generating medical image viewsfor diagnosis. A workflow arranges these activities into a process flowaccording to the order of performing each activity. Each of theactivities in the workflow has a clear definition of its functions, theresource required in performing the activity, and the inputs receivedand outputs generated by the activity. Each activity in a workflow isreferred to as a workflow stage, or a workflow element. Withrequirements and responsibilities clearly defined, a workflow stage of aworkflow is designed to perform one specific task in the process ofaccomplishing the goal defined in the workflow. For many medical imagestudies, the patterns of activities to produce medical image views fordiagnosis are usually repetitive and clearly defined. Therefore, it isadvantageous to utilize workflows to model and document real lifemedical image processing practices, ensuring the image processing beingproperly performed under the defined procedural rules of the workflow.The results of the workflow stages can be saved for later review or use.

In one embodiment, a workflow for a specific medical image study ismodeled by a workflow template. A workflow template is a template with apredefined set of workflow stages forming a logical workflow. The orderof processing an activity is modeled by the order established among thepredefined set of workflow stages. In one embodiment, workflow stages ina workflow template are ordered sequentially, with lower order stagesbeing performed before the higher order stages. In another embodiment,dependency relationships are maintained among the workflow stages. Undersuch arrangement, a workflow stage cannot be performed before theworkflow stages it is depending on being performed first. In a furtherembodiment, advanced workflow management allows one workflow stagedepending on multiple workflow stages, or multiple workflow stagesdepending on one workflow stage, etc.

The image processing operations receive medical image data collected bythe medical imaging devices as inputs, process the medical image data,and generate metadata as outputs. Metadata, also known as metadataelements, broadly refers to parameters and/or instructions fordescribing, processing, and/or managing the medical image data. Forinstance, metadata generated by the image processing operations of aworkflow stage includes image processing parameters that can be appliedto medical image data to generate medical image views for diagnosticpurpose. Further, various automatic and manual manipulations of themedical image views can also be captured as metadata. Thus, metadataallows the returning of the system to the state it was in when themetadata was saved.

After a user validates the results generated from processing a workflowstage predefined in the workflow template, workflow management system205 creates a new scene and stores the new scene to the workflow scene.Workflow management system 205 also allows the updating and saving ofscenes during user adjustments of the medical image views generated fromthe scenes. Further detailed information concerning workflow managementsystem 205 can be found in co-pending U.S. patent application Ser. No.12/196,099, entitled “Workflow Template Management for Medical ImageData Processing,” filed Aug. 21, 2008, now U.S. Pat. No. 8,370,293,which is incorporated by reference herein in its entirety.

Referring back to FIG. 2, according to one embodiment, image processingserver 110 includes advanced image processing system 140 and automaticimage processing system 150. Automatic image processing system 150 maybe layered on top of, or integrated with, an existing or new advancedmedical image processing software system (e.g., advanced imageprocessing system 140) to simplify or automate the use of the medicalimage processing system, which may be implemented in software, hardware,or a combination of both. The automatic image processing system 150 maybe implemented in a form of an image processing wizard, as describedabove.

According to one embodiment, automatic image processing system 150provides a user friendly interactive graphical user interface. Theautomatic image processing system 150 allows a user to interact with theimage processing software hosted by image processing server 110 based ona set of easily understandable processing stages to perform certainmajor or common or popular image processing operations on an image orimages, without having to fully understand specific steps and/or imageprocessing parameters for processing the image. The automatic imageprocessing system 150 may interact with the user through a series ofquestions and receive user inputs as a part of answers from the user todetermine the user's intent. Based on the user interaction with theautomatic image processing system 150, one or more image processingoperations may be determined and recommended to the user via automaticimage processing system 150. Alternatively, automatic image processingsystem 150 may automatically perform the image processing without userintervention. The user can select one or more of the recommended imageprocessing operations for processing the image. Based on a userselection of one or more of the image processing indicators, one or moreimage processing parameters associated with the selected imageprocessing indicators are automatically determined without userintervention and without having the user providing the same parameters.

Based on the image processing parameters received by automatic imageprocessing system 150, according to one embodiment, one or more imageprocessing commands are generated and transmitted from automatic imageprocessing system 150 to image processing engine 104. In response to theimage processing commends, image processing engine 104 of imageprocessing server 110 processes the image based on the image processingparameters and generates a new or updated image. The new image mayrepresent a different view of the same medical data associated with theoriginal image. The new image is then transmitted from the imageprocessing engine 104 back to the automatic image processing system 150,which in turns transmits the new image to a client device to bepresented to the user. The user is prompted whether the user issatisfied with the new image. If the user is unsatisfied with the newimage, the system may interact with the user for more user inputsconcerning the new image and further adjust the image processingparameters and the image processing operations may be iterativelyperformed. As a result, a user does not have to fully understanding howto utilize the advanced image processing system, although the advancedimage processing system may also be available for advanced users.

FIG. 3 is a block diagram illustrating an example of an image processingclient application according to one embodiment of the invention. Imageprocessing client 300 may be implemented as a part of image client 121in FIG. 1 and/or client applications 211-212 of FIG. 2. Referring toFIG. 3, image processing client 300 is communicatively coupled to imageprocessing server 110 over a network. In one embodiment, Imageprocessing server 110 includes advanced image processing system 140 andautomatic image processing system 150 coupled to image processing logic310, which may be implemented in software, hardware, or a combination ofboth.

Advanced image processing system 140 includes functionalities similar tothose described in the above incorporated-by-reference U.S. patentapplication. Advanced image processing system 140 presents advancedimage processing graphical user interface (GUI) 301 at client 300 andincludes corresponding advanced image processing module, logic, orprocessor 303. Advanced image processing GUI 301 is used to presentdetailed processing interface to allow an advanced or experience user tospecifically specify detailed parameters for processing a medical image.GUI 301 may include multiple input fields or controls to receivespecific image processing parameters. For example, the user may usetools to take manual measurements, identify anatomy, segment anatomyetc. More tool functions will be described in details further belowherein. The image processing indicators provided by a user are receivedvia GUI 301 and processed by advanced image processing module 303. Theimage processing indicators may be determined based on the userinteractions with advanced image processing GUI 301. For example, when auser clicks an item or tag displayed by GUI 301, the click event oraction of the specific item or tag is transmitted from client 300 toserver 110, and analyzed and interpreted by advanced image processingmodule 303 based on the underlying information associated with the itemor tag to determine an image process operation associated with thatparticular item or tag. Advanced image processing module 303 thengenerates one or more image processing commands. The image processingcommands are then transmitted to image processing engine 104 forprocessing medical images, using image processing tools or functions311-313. An example of advanced image processing GUI 301 may be similarto the one as shown in FIG. 8H, from which a user can manually specifymore input parameters.

Similarly, according to one embodiment, automatic image processingsystem 150 presents automatic image processing GUI 302 at client 300.GUI 302 provides a simple or automated user interface to interact with auser, for example, via a wizard, to guide the user to “walk” through aseries of the major steps of image processing operations withoutrequiring the user to know or provide detailed information of theprocessing operations. For example, GUI 302 may prompt the user with aseries of questions and based on the answers received from the user,image processing module 304 analyzes the user interaction to determinethe user intent. Based on the analysis, image processing module, logic,or processor 304 determines a list of one or more image processingoperations. The image processing operations may be those that are morecommon or popular amongst the users based on the similar userinteractions. Alternatively, the image processing operations may bedetermined based on a set of rules that is formulated based on the priorinteractions or user preferences of the user.

In addition, processing module 304 determines a set of image processingparameters based on the image processing operations. The imageprocessing commands and parameters are determined by processing module304 automatically without user interventions, including selecting aproper image processing tool or tools. The image processing parametersor tools are not exposed or visible to the user and the user does nothave to fully understand the parameters. However, if the user wishes toset those parameters or use the tools specifically tailored to his/herneeds, the user may want to utilize advanced image processing GUI 301 ofadvanced image processing system 140. According to one embodiment, thecommands and parameters are then provided to image processing engine 104for processing the images.

The commands may further include other information, such as, forexample, a patient ID, a body part ID, if an image is associated with amedical procedure or appointment, a procedure ID or appointment ID, etc.For example, from automatic image processing GUI 302, a user may choosea procedure from within a list of procedures which a patient hasundergone. For example, a patient may have a virtual colonoscopy, an EKGand an eye exam listed as procedures he has undergone. The user maychoose the virtual colonoscopy. In doing so, automatic image processingGUI 302 of client 300 transfers information to automatic imageprocessing module 304 such as the patient ID, the colon, colonoscopy,date of the procedure or other pertinent information. The automaticimage processing module 304 then uses this information to identify theassociated image series, as well as the image processing toolsassociated with the image series. The server 110 also processes theimage series and presents the results to the user via automatic imageprocessing GUI 302. The results may include several images includingdifferent views, any polyps identified, the size of the polyps, etc.

According to one embodiment, both advanced image processing system 140and automatic image process system 150 may access the image processingfunctions (e.g., libraries, routines, tools, etc.) of image processingengine 104 via a set of application programming interfaces (APIs) orcommunication protocols (if image processing engine 104 is a remotesystem over a network). When advanced image processing system 140 isutilized, according to one embodiment, advanced image processing GUI 301may be presented, for example, similar to the graphical user interfaceas shown in FIG. 8H, to allow the user to specify detailed imageprocessing parameters for processing a specific image selected by theuser. The advanced image processing module 303 of advanced imageprocessing system 140 processes the user inputs received from theadvanced graphical user interface 301 and formulates one or more imageprocessing commands with a set of image processing parameters that aregenerated based on the user inputs. The advanced image processing module303 of advanced image processing system 140 then sends the commands, forexample, via the APIs, to image processing engine 104 to process theimage. Image processing engine 104 may be implemented using dedicatedgraphics or image processing hardware, such as graphics processing units(GPUs).

When automatic image processing system 150 is utilized, according to oneembodiment, a simplified graphical user interface (e.g., wizard) 302 ispresented at client device 300 of the user to walk the user through aseries of simple steps or interactive questions without requiring theuser to specify the detailed operational image processing parameters.The automatic image processing module 304 is to automatically determinethe detailed image processing parameters based on the user interactionwith the simplified graphical user interface 302. The user interactionmay be captured and received by GUI 302 and transmitted to server 110,for example, as part of image processing indicators. A set of imageprocessing commands is generated based on the user interaction or imageprocessing indicators and sent to image processing engine 104 forprocessing the image. Alternatively, the automatic image processingmodule 304 of automatic image processing system 150 determines theparameters and passes the parameters to the advanced image processingsystem 140, just as the advanced image processing system would havereceived from a user via its corresponding graphical user interface 301.The advanced image processing module 303 in turn communicates with imageprocessing engine 104 on behalf of automatic image processing system150. Note that some or all of the components as shown in FIG. 3 may beimplemented in software, hardware, or a combination thereof.

According to one embodiment, an example of image processing operationsmay be a flythrough procedure for identifying and measuring a polyp inan image. Typically, when a user uses the advanced image processingsystem 140, the corresponding advanced image processing GUI providesmore fields and buttons, such as image processing tools similar to thoseas shown in FIG. 8H, to allow the user to specify the parameters andtools to be used to process an image. As a result, the user has to be anadvanced user who is familiar with the software and/or tools. Forexample, a user may have to select, amongst others, the tool of “pickpolyp” to identify a polyp from an image, location, and/or size or shapeof a polyp. Based on the selection, a polyp identifier, as well as itslocation within the image may then be transmitted to an image processingengine to measure the size of the selected polyp, etc.

When automatic image processing system 150 is utilized, a user does nothave to specify at least some of the parameters and/or tools as requiredwhen using advanced image processing system 140. Once the useridentifies a patient (possibly by being logged into their EMR account)and either identifies a body area (abdomen) or procedure (flythrough),according to one embodiment, automatic image processing system 150generates the flythrough results automatically, including initialidentification of polyps. The user may then provide feedback on thenumber and location and size of the polyps which have been automaticallydetected, located, and measured. In one embodiment, the user is given achance to review whether the automatic image processing system hasaccurately performed the intended operations. For example, when an imageprocessing operation associated with a flythrough of polyps, whendisplaying the results, the automatic image processing system promptsthe user whether the polyps have been correctly identified (e.g., numberof polyps), as well as their locations and sizes, etc. A set of simplechoices or questionnaires may be presented to the user for further userinputs. Based on the further user inputs, the automatic image processingsystem can reprocess the image according to the further user inputs. Itis possible if the user clicks on the abdomen, there may be more thanone imaging procedure associated with the abdomen and the user may needto identify flythrough as the result he/she wants to see. In oneembodiment, automatic image processing system 150 invokes imageprocessing engine 104 to automatically determine a location of polyps,number of polyps, size and volume of polyps, and possible change inpolyps amongst multiple images.

Image processing engine 104 can differentiate between different tissuetypes and densities. It also knows generally where certain anatomy isbased, for example, on landmarks or shape atlases etc. For example,image processing engine 104 includes certain graphics logic or functions(e.g., image processing algorithms or routines) to process (e.g.,comparing, filtering, transforming) the pixel values to detect an edgeor area of a particular shape, perform a pattern recognition of thedetected shape or area to identify a particular type of body part ororgan (e.g., heart, polyp), and measure the size of the shape or area,etc. Once it segments (draw outlines) and identify shapes, it can takemeasurements and perform a predetermined algorithm, etc. It isprogrammed to show certain types of views depending on what it finds.For example, it would show the colon flythrough if polyps were found, ordimensions of heart vessels if stenosis were found, or various tumorslices if tumors were found. The user choosing a body area and/orprocedure helps the system narrow down its analysis.

Another example of image processing operations is stenosis measurement(e.g., in a heart vessel). Again, when advanced image processing system140 is utilized, a user has to select the proper tool for stenosismeasurement, as well as, the location or size of the measurement to beperformed. When automatic image processing system 150 is utilized, theuser only has to identify the heart, and/or “stenosis” information toreceive the results. In response, the backend system, such as imageprocessing engine 104, is automatically able to locate the bloodvessels, measure them, locate a narrowing, and perform an algorithm orcalculation on what percentage the narrowing is. The user can adjustthese measurements if he/she wants to, for example, moving the outlineof the vessel so that it is more accurate. The system can automaticallydetermine vessel segmentation (outline) for all vessels, vesseldiameters, diameter narrowing, percent diameter narrowing, possiblechange in stenosis amongst multiple images.

Another example of image processing operations is related to tumorvolume measurement. For example, for a brain tumor, a user can click onthe head, and depending on what imaging procedures have been done on thebrain, the automatic image processing system automatically generates thebrain tumor results (if this is the only procedure), or asks the user tochoose which procedure he/she wants (for example, there may be ananeurism scan also). The system can automatically find the location ofthe tumor, draw the volume outline (segment the tumor) and provide thevolume of the tumor.

FIG. 4 is a block diagram illustrating an example of an automatic imageprocessing module according to one embodiment of the invention.Referring to FIG. 4, automatic image processing module 304 includesdialog engine 401 and command processing module 402. These modules maybe implemented in software, hardware, or a combination thereof. In oneembodiment, dialog engine 401 communicates via GUI 302 with a user in adialog format by presenting a series of questions based on a set ofprocessing rules 403. The answers received from the user may be analyzedby dialog engine 401 or another analysis module (not shown) to determinea set of image processing operations. The image processing operationsmay be determined based on body part, procedure, appointment, conditionetc. A user intent is then determined based on the answers received fromthe user. The user intent may be interpreted based on user preferences404 or prior user interactions in addition to data such as body part,procedure, appointment, condition etc.

According to one embodiment, GUI 302 presents various user selectabletags, which may be associated with body areas, medical procedures,medical appointments, and/or medical conditions, for example, as shownin FIGS. 7A-7D. Each of the tags displayed may be associated with orinterpreted as an image processing indicator that indicates a specifictype or types of image processing operation or operations. In responseto a user selection of one or more image processing indicators, a set ofimage processing parameters may be automatically determined, withoutuser intervention, by command processing module 402. The parameters arethen utilized by image processing server 110, for example, via imageprocessing logic 310. For example, when automatic image processingsystem 150 is invoked by a user, dialog engine 401 may prompt the userfor what the user wants to do. The user can select the images based onbody parts, medical procedures, medical appointments, and medicalconditions of a patient. Based on the user selection, dialog engine 401can present further questions concerning the image processing operationsthat may be needed or recommended, etc. to walk the user through anautomatic processing timeline, without having the user deep dive and getoverwhelmed by the image processing details.

FIG. 5 is a block diagram illustrating an example of processing rules ofan automatic image processing system according to one embodiment of theinvention. Referring to FIG. 5, rules 500 may be implemented as a partof processing rules 403 stored in a persistent storage device (e.g.,hard drive) and/or loaded into a system memory accessible by automaticimage processing module 304 at server 110 of FIG. 4. In this example,rules 500 include tables or data structures 501-505 to determine a setof one or more image processing operations based on a variety ofcategories of information, which may be represented by image processingindicators received from a client device, where may be determined basedon user interactions with respect to content presented in a graphicaluser interface displayed at the client device. An image processingindicator may represent a patient ID, a body area ID, a medicalprocedure ID, a medical appointment ID, a medical condition ID, or acombination of any of these IDs. For example, the image processingoperations may be determined based on tags via data structure 501,medical procedures via data structure 502, body parts via data structure503, and medical conditions via data structure 504. Once the imageprocessing operations are determined, the associated image processingcommands and their respective parameters can be identified via datastructure 505. The information stored in data structures 501-505 may beimplemented in a database and user configurable, for example, by anadministrator, via a configuration interface of server 110. Suchinformation may be stored in and retrievable from server 110 via a setof application programming interfaces (APIs) or communication protocolsover a network. Similarly, any user preferences or prior interaction mayalso be recorded in the server 110.

FIG. 6 is a flow diagram illustrating a processing flow of an automaticimage processing system according to one embodiment of the invention.Referring to FIG. 6, at block 601, a medical record of a patient isdisplayed, where the medical record may be obtained from a MRCS server.The medical record may be obtained by a dedicated module, such as a dataintegration engine, of image processing server 110 that iscommunicatively coupled to the MRCS server, where the data integrationengine communicates with various information sources to retrieve and tointegrate various types of medical and/or image information. Dependentupon what the user wants, the medical record can be displayed in a formof medical appointments in block 602, body areas in block 603, medicalprocedures in block 604, and medical conditions in block 604, or in anyother appropriate manner. At block 606, a user selection is received.Image processing operations are determined and performed by an imageprocessing server at block 607. The processing results are presented tothe user at block 608 and if the user is unsatisfied with the results,the image processes can be iteratively performed via block 609. At block610, certain export options can be presented to the user for selectionat block 611, and the data can be displayed or exported at block 612.

FIGS. 7A-7D are screenshots illustrating examples of graphical userinterface of an automatic image processing system according certainembodiments of the invention. GUIs as shown in FIGS. 7A-7D may begenerated by server 110 and received and presented by client 105 of FIG.3. User interactions with the GUIs are captured and transmitted from theclient device to image processing server 110. The user interactions arethen interpreted or analyzed by image processing server 110 and inresponse to the user interaction, image processing server 110 performsproper actions, such as, image processing operations, informationretrieval operations, and data processing and/or integration operations,and returns processing results back to the client. The processingresults are presented and/or integrated with the existing information ata display device of the client.

Referring to FIG. 7A, the GUI includes a first display area 701 todisplay patient identifying information, a second display area 702 todisplay detailed information about the patient, and a third display area703 to display an image processing timeline. The GUI shows one way toaccess imaging data that is associated with a particular patient. Inthis example, a user can access medical data via body areas. Arepresentation of a human body 710 is shown with selectable tags711-714, such as CTA Abdomen tag 711 and Flythrough tag 713, whereimaging procedures have been performed on this patient. In this example,this patient has had imaging procedures associated with theabdomen/Pelvis, but not with other areas of the body, such as the brain.If imaging data were available for the brain, a tag would show up in alocation associated with the brain area. The medical record informationsuch as patient information may be obtained by server 110, where server110 may be integrated with a medical record server or communicativelycoupled to the medical record server.

These image tags 711-714 are selectable, which when selected will bringthe user to another screen which will provide more information and/orquestions about the particular body part/procedure selected. Forexample, when a user selects or clicks one of the tags or items, asignal representing the selected tag or item is transmitted from theclient to image processing server 110. In response to the signal (e.g.,image processing indicator), image processing server 110 performs properactions, which may include an image processing operation and/orinformation retrieval operation. The results of the actions are thentransmitted from image processing server 110 back to the client andpresented to the user via the GUI interface. Across the top of the GUIis timeline 703, or process line, or workflow, of the image dataviewing/processing process. Timeline 703 includes multiple graphicalrepresentations 721-724 representing different processing stages withinthe timeline. In this example, the user is currently at the “what do youwant to do?” stage 721. At this stage, the user chooses what type ofimage data he/she wants to view/process. The timeline 703 shows thatsubsequent steps will include processing image stage 722, showing resultstage 723, determining whether the results are acceptable orunacceptable stage 724, and finishing the process at the end.

Referring now to FIG. 7B, this GUI shows another way to access imagingdata that is associated with a particular patient. The method ofaccessing image data shown in FIG. 7B is by appointment history. Forexample, a user may log into image processing server 110 and elect toview images based on medical appointment history, for example, byclicking a corresponding link or button (not shown). A list of pastappointments 730 is shown, along with a link to image data if any imagedata is associated with the appointment. In this example, the patienthas had a CT scan and a Colonoscopy, both of which have associated imagedata which is represented by a graphical representation 731, in thisexample, an “eye” icon. Other appointments, such as routine checkups, inthis example, do not have associated image data. The graphicalrepresentation 731 in this example are selectable and will bring theuser to another screen which will provide more information and/orquestions about the particular image data associated with theappointment.

Referring now to FIG. 7C, this GUI shows another way to access imagingdata that is associated with a particular patient. The method ofaccessing image data shown in FIG. 7C is by procedure history 740. Alist of past procedures is shown, along with a link to image data if anyimage data is associated with the procedure. In this example, thepatient has had a CT scan and a Colonoscopy in addition to otherprocedures. These procedures have associated image data which isrepresented by a graphical representation such as an “eye” icon. The“eye” icons in this example are clickable and will bring the user toanother screen which will provide more information and/or questionsabout the particular image data associated with the procedures.

Referring now to FIG. 7D, this GUI shows another way to access imagingdata that is associated with a particular patient. The method ofaccessing image data shown in this Figure is by conditions. A list ofconditions associated with this patient is shown, along with a link toimage data if any image data is associated with the condition. In thisexample, the patient has been diagnosed with IBS and Palpitations. Bothof these conditions have associated image data which is represented by agraphical representation or link such as an “eye” icon. The “eye” iconsin this example are clickable and will bring the user to another screenwhich will provide more information and/or questions about theparticular image data associated with the condition.

As shown in FIGS. 7A-7D, a user can utilize an automatic imageprocessing GUI of the automatic image processing system (e.g., system150) to start with the information of a medical record of a patient.When a medical record is presented, the automatic image processingmodule 304 is to determine whether there are images associated with themedical information of the medical record. If so, automatic imageprocessing module 304 causes the corresponding automatic imageprocessing GUI to display a graphical representation at the clientdevice indicating that one or more images are available for thatparticular medical record data (e.g., body area, procedure, condition,appointment).

A user can access the associated medical images by activating (e.g.,clicking) the corresponding links or graphical representations. Afterthe user activates on the link to image data (in this example, clickingon an “eye” icon), the user may be brought directly to image processingresult stage 723, or may be asked more questions. For example, if thereis more than one sets of images associated with the body part,appointment, procedure, and/or condition etc., the user may be asked tochoose which set of images he/she wants to analyze. Also, there may beother questions relating to settings, parameters, processing preferencesetc. for example, the user, after clicking on images associated with acolonoscopy, may be asked what type of views he/she prefers. The usermay be given the option to have the system “remember” his/herpreferences so he/she doesn't have to answer the question every timehe/she wants to analyze similar image sets. For example, the system maypresent a checkbox which states “always use this setting for this typeof image” or similar. Such user preferences may be transmitted back tothe image processing server and stored therein. The user preferences canbe retrieved when the same user subsequently logs into the imageprocessing server.

According to one embodiment, the interaction, i.e., dialog, between thesystem and the user may be handled by dialog engine 401 of FIG. 4. Basedon the user interactions, the user behaviors are analyzed to determinethe proper actions to be taken. For example, based on the particularbody area, body part, medical procedure, and/or medical condition,certain image processing operations may be identified that can beperformed on the associated images. The image processing operations maybe recommended to the user. Alternatively, such image processingoperations may be automatically performed without user intervention orknowledge. In this embodiment, an analysis module (not shown) analyzesthe user interactions provided by the dialog engine and determines oneor more image processing operations (e.g., measurements). The underlyingcommand processing module 402 generates the proper image processingcommands and transmits the commands to image processing engine 104 forimage processing. When the image processing results are received fromimage processing engine 104, the results are then transmitted back toclient 105 and presented via the automatic image processing GUI 302 aspart of show result stage 724.

FIGS. 8A-8K are screenshots illustrating examples of graphical userinterface of an automatic image processing wizard according certainembodiments of the invention. GUIs as shown in FIGS. 8A-8K may begenerated by automatic image processing system 150 and received andpresented by automatic image processing GUI 302 of FIG. 3. Userinteractions with the GUIs are captured and transmitted from the clientdevice to image processing server 110. The user interactions are theninterpreted by image processing server 110 and in response to the userinteraction, image processing server 110 performs proper actions, suchas, image processing operations, information retrieval operations, anddata processing and/or integration operations, and returns processingresults back to the client. The processing results are presented and/orintegrated with the existing information at a display device of theclient.

FIG. 8A shows an example of a results screen that is part of show resultstage 823, indicated as a current processing stage. In this example, theuser has clicked on an image data link associated with the abdomen, andmore specifically, a colonoscopy flythrough, of the patient. The imagedata link may be presented in any of GUIs as shown in FIGS. 7A-7D. Asignal representing the user action (e.g., clicking the image data link)is transmitted from the client to the image processing server 110. Forexample, the signal may include an identifier identifying acorresponding body part (e.g., abdomen) and/or a medical procedure(e.g., colonoscopy flythrough). The image processing server processesthe signal, performs proper operations, and generates results. Theresults are then transmitted from image processing server 110 back tothe client to be presented to the user. In this example, the resultsshow three identified polyps in display area 801, along with severaldifferent views of the intestines and polyps, and quantitative data forthe polyps, such as volume, in display areas 802-804. In this example,the image processing results were performed automatically, without userintervention, by the image data processor at the image processing server110. The user may have the ability to set user preferences or settingsthat are applied to the automatic image processing.

If the user highlights one of the polyps in display area 801, he/she cansee the location in the anatomy where the polyp was found from differentperspectives, as well as quantitative data, in display areas 802-804. Inthis way, the results can be fully analyzed and reviewed. Note that thetimeline 703 on the top of the screen shows where in the process theuser is. Since this is the results screen, the timeline shows that theprocessing has already taken place and the user is now viewing theresults of the image processing at stage 723. When the user is finishedreviewing the results, he may click on the “next” button 805 to go tothe next step, confirm result stage 824.

FIG. 8B shows an example of a wizard pop-up window 806 which helps theuser either confirm the image data processing results, or improve theresults, which is a part of confirm stage 724 in response to anactivation of next button 805. The wizard pop window 806 is generated bythe image processing server 110 and received at a client to prompt theuser to confirm whether the image processing results are correct oraccurate. In this example, the user has determined that the imageprocessing results included too many polyps. This could happen of theimage processing system miss-identified one or more polyps. After theuser hits the “next” button 807, he or she may be taken to a screen suchas the one shown in FIG. 8C, which shows the automatic image processingsystem asking the user which polyp(s) he/she does not believe should beidentified as such.

FIG. 8D shows an example of a screen that the user may see to helphim/her identify the incorrectly identified polyp(s). In this example,the user can click on any one of the 3 polyps to identify it as theincorrect one, where these polyps are automatically identified by thewizard. The user can also view more detail associated with anyindividual polyp by highlighting the polyp. In this example, the userhas highlighted polyp 1 to view more detail/views. In response, thevolume of the polyp 1 is shown in a different display area, which ismeasured by the image processing system automatically. The user clickson the number 2 to identify polyp 2 as the incorrectly identified polyp.After the user identifies polyp 2, he/she may be shown a screen similarto that in FIG. 8E, asking the user to confirm that he/she has chosenpolyp number 2 to be removed.

FIG. 8F shows a screenshot similar to that in FIG. 8B. Note that in thisexample the user is still not satisfied with the results, but haschecked a different box: “I want to see the individual steps todetermine where things went wrong”. This option may be available to theuser based on user privileges. This will take the user to a moreadvanced screen, such as FIG. 8G, which will allow the user to evaluateeach step that the system has processed and review and make changes toany steps that require changes.

FIG. 8G shows image representations of the various steps that the imageprocessing system has performed to obtain the image processing result.This example GUI shows three steps but there may be fewer or more stepsshown. The three steps shown here depict the identification of thecolon, the colon centerline, and the identification of potential polypsin the colon, which are identified automatically by the automatic imageprocessing system. The user may click on any of these steps to changethe results. If the user changes the results of one step, the changeddata will be propagated through the following steps when the image datais again processed. For example, if the user were to change the colondefinition in step one, by, for example, extending it or reducing itslength, a different centerline and possibly different polyps would beidentified in subsequent steps as a result in the changed colon length.The data is passed from a step to subsequent steps in meta-dataassociated with the image data. In this example, the user has chosenstep 3 to analyze further.

FIG. 8H shows a more advanced screen for analyzing and possibly alteringthe image data. In this example, the user is reviewing step 3. The userhas access to more advanced tools on this screen and can define variousparameter of the polyps. According to one embodiment, access to thisscreen may be limited to users who have been trained to use theseadvanced image processing tools (e.g., certain user privileges). Theuser has the ability to move among different steps by clicking thethumbnails that have now appeared in the timeline at the top of thescreen. Advanced tools may be presented for the different steps,although the tools may be different for each step. The availability ofthe tools may be determined by the image processing server based on thetypes of the images, or the body parts, medical procedures, medicalconditions associated with the images. The user may choose to stepthrough every step to confirm that the processing has been doneaccurately. Note that the GUI as shown in Figure H may be displayed byadvanced image processing user system 140, which may be invoked from theautomatic image processing user system 150.

FIG. 8I shows the results of the user's changes to the various imageprocessing steps. If no changes were made, the results will not change.FIG. 8J shows a results confirmation wizard pop-up window again. Thistime the user has indicated that the results are OK. FIG. 8K shows someexample options available to the user after the image processing resultshave been confirmed. Options include report generation, sharing theresults with other physicians, patients or others, and exporting thedata in various formats including xml, csv, html and other formats.Other options may be available.

FIGS. 9A-9F are screenshots illustrating examples of graphical userinterface of an automatic image processing system according alternativeembodiments of the invention. The GUIs as shown in FIGS. 9A-9F may begenerated by image processing server 110 and received and represented ata client device. User interactions with the GUIs are captured andtransmitted from the client device to image processing server 110. Theuser interactions are then interpreted by image processing server 110and in response to the user interaction, image processing server 110performs proper actions, such as, image processing operations,information retrieval operations, and data processing and/or integrationoperations, and returns processing results back to the client. Theprocessing results are presented and/or integrated with the existinginformation at a display device of the client. FIG. 9A, shows anotherexample of how a user may use the client 105. The method of accessingimage data shown in this embodiment is by body area. A graphicalrepresentation of a human body is shown and image tags, such as SAT lungand Flythrough, are shown associated with the different body areas 901.These image tags 901 are selectable or clickable and will bring the userto another screen, which may provide a patient list relating toparticular body part/procedure selected. From the patient list, the usercan retrieve one or more images that are associated with a particularpatient in the list, where the images are also related to thecorresponding body area of the selected image tag.

Across the top of this screen is a timeline, or process line, orworkflow, of the image data viewing/processing process 903, similar totimeline 703 of FIG. 7A. The user is currently at the “what do you wantto do?” stage 911. At this stage, the user can choose the type of imagedata he/she wants to view/process, for example, by selecting one or moreof the image tags displayed in display area 902. The timeline 903 showsthat subsequent steps will include choosing a patient, choosing moredetails (e.g., detail stage 913) relating to the images/patient,processing the images, showing the results, determining whether theresults are acceptable or unacceptable, and finishing the process.

FIG. 9B shows a screen representing the “with what/whom” stage 912 inthe timeline 903. For example, if the “flythrough” tag 904 of FIG. 9A isselected, an identifier identifying a flythrough procedure istransmitted from the client to the image processing server 110. Inresponse, the image processing server 110 identifies or generates basedon the identifier a list of patients who have an image associated withthe flythrough procedure. The result is returned from the imageprocessing server to the client device and the user can see a list ofpatients 921 who have images on records associated with a flythroughprocedure. Also shown are different series of images 922 associated witha given patient, as well as a preview panel 923 showing the imagesassociated with each series, which are also generated by the imageprocessing server 110 and received at the client device. The user cansearch through the patients/series to find the patient/series he/shewants. Note that the information as shown in FIG. 9B may be retrieved bythe underlying logic (e.g., components or logic 401-402 of FIG. 4) fromimage processing server 110 and/or medical data server 115 of FIG. 1 inresponse to the user inputs, without requiring the user to know thedetails. The relationships between patients and images of a particularbody area may be stored in a database maintained by image processingserver 110 and/or MRCS server 115 of FIG. 1. The user also has theoption of associating certain series of images with a procedure in thefuture.

FIG. 9C shows a screen representing the “details” stage 913 in thetimeline, which may be displayed in response to an activation of the“next” button from FIG. 9B. Here the user can set preferences includinghow to process the image(s), etc. The user can also set the preferencesas defaults so that this step may be skipped in the future. The userpreferences may be stored as part of user preferences 404 of FIG. 4 andtransmitted back to image processing server 110 to be stored therein,for example, as part of a user account or user profile associated withthe user in a persistent storage device such as a hard disk. As aresult, when the user subsequently logs into the image processing server110, for example, from the same or a different client device, the usercan retrieve the user preferences from the image processing server.

FIG. 9D shows an example of show result stage 914. In this example, theresults for the flythrough show 3 identified polyps, along with severaldifferent views of the intestines and polyps, and quantitative data forthe polyps, such as volume. In this example, the image processingresults were performed automatically, without user intervention, by theimage data processor (e.g., image processing server and/or client). Ifthe user selects or highlights one of the polyps, he/she can see thelocation in the anatomy where the polyp was found from differentperspectives, as well as quantitative data. In this way, the results canbe fully analyzed and reviewed. Notice that the timeline 903 on the topof the screen shows where in the process the user is. Since this is theresults screen 914, the timeline 903 shows that the processing hasalready taken place and the user is now viewing the results of the imageprocessing. When the user is finished reviewing the results, he mayclick on the “next” button to go to the next step, confirm result stage.

FIG. 9E shows an example of a wizard pop-up window which helps the usereither confirm the image data processing results, or improve theresults. In this example, the user has indicated that the imageprocessing results are satisfied. FIG. 9F shows some example optionsavailable to the user after the image processing results have beenconfirmed. Options include report generation, sharing the results withother physicians, patients or others, and exporting the data in variousformats including xml, csv, html and other formats. Other options may beavailable.

Note that the user interactions with the GUIs as shown in FIGS. 8A-8Kand FIGS. 9A-9F may be captured, interpreted and analyzed by theunderlying logic such as those as shown in FIG. 4. Based on theanalysis, one or more commands are generated by the underlying logic andcommunicated with the image processing server 110 and/or MRCS server115. A user does not have to fully understanding the underlyingoperations, parameters, and image processing commands. However, anadvanced user can also invoke advanced image processing system 140 asneeded. Automatic image processing system 150 provides an alternativeway to access image processing tools based on user preferences and/orprivileges.

Although GUIs for the user are generally shown here as clickable objectson a computer screen, other types of input devices may be used tointeract with the GUI. For example. The user may use voice and/or motionand/or gestures to interact with the GUI. These types of input devicesare particularly useful in a sterile environment such as an operatingroom.

FIG. 10 is a flow diagram illustrating a process performed by anautomatic image processing system according to one embodiment of theinvention. Process 1000 may be performed by processing logic which mayinclude software, hardware, or a combination thereof. For example,process 1000 may be performed by automatic image processing system 150of FIG. 1. Referring to FIG. 10, at block 1001, an image processingindicator is received at an automatic image processing system from aclient device. The image processing indicator identifies at least one ofa patient, a body area of a patient, a medical procedure, a medicalappointment, a timeframe, and a user of the client device. At block1002, processing logic automatically identifies a first image based onthe image processing indicator, for example, based on a set of rules(e.g., rules as shown in FIG. 5). The processing logic determines one ormore image operations, the associated processing parameters, and/ordimension, etc. An image processing engine is invoked to process thefirst image by performing the determined image processing operationsbased on the associated processing parameters. At block 1003, processinglogic transmits a second image resulted from the image processingoperations to the client device to be presented on a display of theclient device. At block 1004, a request is transmitted to the clientdevice to cause the client device to prompt the user whether the user issatisfied with the second image, optionally with one or more options forfurther user inputs. At block 1005, in response to a user input receivedfrom the client device indicating that the user is not satisfied withthe second image, processing logic determines and transmits to theclient device one or more remedial options to allow the user to select aremedial action to reprocess the first image.

As described above, a variety of image processing tools can be accessedby a user using the automatic image processing system, for example, asan image processing wizard. The following are examples of medical imageprocessing tools that may be included as part of the image processingsystem described above. These examples are provided for illustrativepurposes and not intended to be a limitation of the present invention.

Vessel Analysis tools may include a comprehensive vascular analysispackage for CT and MR angiography capable of a broad range of vascularanalysis tasks, from coronary arteries to aortic endograft planning andmore general vascular review, including carotid and renal arteries.Auto-centerline extraction, straightened view, diameter and lengthmeasurements, CPR and axial renderings, and Vessel Track mode forautomated thin-slab MIP may be included.

Calcium scoring tools may include Semi-automated identification ofcoronary calcium with Agatston, volume and mineral mass algorithms. Anintegrated reporting package with customization options may be included.

Time-dependent analysis tools may include time-resolved planar orvolumetric 4D brain perfusion examinations acquired with CT or MR. TheTDA tools may support color or mapping of various parameters such asmean enhancement time and enhancement integral, with semi-automatedselection of input function and baseline, to speed analysis. TDA toolsmay support rapid automated processing of dynamic 4D area-detector CTexaminations to ensure interpretation within minutes of acquisition.

CT/CTA (Computed tomography angiography) subtraction tools are used inthe removal of non-enhancing structures (e.g. bone) from CT angiographyexaminations, the CT/CTA option includes automatic registration of pre-and post-contrast images, followed by a dense-voxel masking algorithmwhich removes high-intensity structures (like bone and surgical clips)from the CTA scan without increasing noise, aiding with the isolation ofcontrast-enhanced vascular structures.

Lobular decomposition tools identify tree-like structures within avolume of interest, e.g. a scan region containing a vascular bed, or anorgan such as the liver. The LD tool can then identifies sub-volumes ofinterest based on proximity to a given branch of the tree or one of itssub-branches. Research applications include the analysis of the lobularstructure of organs.

General Enhancement & Noise Treatment with Low Exposure tools mayinclude an advanced volumetric filter architecture applying noisemanagement techniques to improve the effectiveness of 3D, centerline,contouring and segmentation algorithms even when source image quality isnot optimum.

The Spherefinder tools perform automated analysis of volumetricexaminations to identify the location of structures with a highsphericity index (characteristics exhibited by many nodules and polyps).Spherefinder is often used with Lung or Colon CT scans to identifypotential areas of interest.

Segmentation, analysis & tracking tools support analysis andcharacterization of masses and structures, such as solitary pulmonarynodules or other potential lesions. Tools may identify and segmentregions of interest, and then apply measurement criteria, such as RECISTand WHO, leading to tabulated reporting of findings and follow-upcomparison. Display and management of candidate markers from optionaldetection engines may be supported, including Spherefinder.

Time volume analysis tools may provide automated calculation of ejectionfraction from a chamber in rhythmic motion, such as a cardiac ventricle.A fast and efficient workflow may be included to enable the user toidentify the wall boundaries of interest (e.g. epicardium andendocardium) and, based on these user-confirmed regions of interest, toreport ejection fraction, wall volume (mass) and wall thickening frommulti-phasic CT data. Tabulated reporting output is included.

Maxillo-facial tools support the analysis and visualization of CTexaminations of the Maxillo-facial region, these tools apply the CPRtool to generate “panoramic” projections in various planes and ofvarious thicknesses, and cross-sectional MPR views at set incrementsalong the defined curve plane.

Applicable to endoluminal CT or MR investigations such as colon, lungs,or blood vessels, the Flythrough tools supports side-by-side review,painting of previously-viewed areas, percent coverage tracking, andmultiple screen layouts including forward, reverse, fisheye and flatvolume rendered views. Tools for contrast subtraction, “Cube View”, andintegrated contextual reporting may be supported. Display and managementof candidate markers from optional detection engines may be supported,including iNtuition's Spherefinder.

The Volumetric Histogram tools allow a volume of interest to besegmented and analyzed for composition. Research applications includethe analysis of low-attenuation regions of the lungs, threshold-baseddivision of tumors into voxel populations, investigation of thrombosedvessels or aneurysms, or other pathology.

Findings workflow tools provide a framework for tracking findings acrossserial examinations. A database holds measurements and key images, andprovides support for structured comparisons and tabulated reporting offindings over time, such as the RECIST 1.1 approach for presentingserial comparisons. The Annotation and Image Markup (AIM) XML schema maybe supported, for automated integration with voice-recognition systemsor clinical databases, and Word-based reports may be derived from thedatabase.

With these tools, any two CT, PET, MR or SPECT series, or any two-seriescombination thereof can be overlaid with one assigned a semi-transparentcolor coding and the other shown in grayscale and volume rendering foranatomical reference. Automatic registration is provided and subtractionto a temporary series or to a saved, third series is possible. Supportfor PET/MR visualization is included.

Certain MR examinations (for example, Breast MR) involve a series ofimage acquisitions taken over a period of time, where certain structuresbecome enhanced over time relative to other structures. These toolsfeature the ability to subtract a pre-enhancement image from allpost-enhancement images to emphasize visualization of enhancingstructures (for example, vascular structures and other enhancingtissue). Time-dependent region-of-interest tools may be provided to plottime-intensity graphs of a given region.

Parametric mapping tools are an enhancement to the Multi-Phase MR tools,the parametric mapping option pre-calculates overlay maps where eachpixel in an image is color-coded depending on the time-dependentbehavior of the pixel intensity. As an example, this tool can be used inBreast MR to speed identification and investigation of enhancingregions.

The MultiKv tools provide support for Dual Energy and Spectral Imagingacquisitions from multiple vendors, providing standard image processingalgorithms such as segmentation or contrast suppression, as well asgeneric toolkits for precise analysis and development of new techniques.

The embodiments described above can be applied to a variety of medicalareas. For example, the techniques described above can be applied tovessel analysis (including Endovascular Aortic Repair (EVAR) andelectrophysiology (EP) planning). Such vessel analysis is performed forinterpretation of both coronary and general vessel analysis such ascarotid and renal arteries, in addition to aortic endograft andelectro-physiology planning. Tools provided as cloud services includeauto-centerline extraction, straightened view, diameter and lengthmeasurements, Curved Planar Reformation (CPR) and axial renderings, aswell as charting of the vessel diameter vs. distance and cross-sectionalviews. The vessel track tool provides a Maximum Intensity Projection(MIP) view in two orthogonal planes that travels along and rotates aboutthe vessel centerline for ease of navigation and deep interrogation.Plaque analysis tools provide detailed delineation of non luminalstructure such as soft plaque, calcified plaque and intra-mural lesions.

In addition, the techniques described above can be utilized in the areaof endovascular aortic repair. According to some embodiments, vascularanalysis tools provided as cloud services support definition of reporttemplates which captures measurements for endograft sizing. Multiplecenterlines can be extracted to allow for planning of EVAR procedureswith multiple access points. Diameters perpendicular to the vessel maybe measured along with distances along the two aorto-iliac paths. Customworkflow templates may be used to enable the major aortic endograftmanufactures' measurement specifications to be made as required forstent sizing. Sac segmentation and volume determination with a“clock-face” overlay to aid with documenting the orientation andlocation of branch vessels for fenestrated and branch device planning,may also be used. Reports containing required measurements and data maybe generated.

The techniques described above can also be applied in the left atriumanalysis mode, in which semi-automated left atrium segmentation of eachpulmonary vein ostium is supported with a single-click distance pairtool, provided as cloud services, for assessment of the major and minorvein diameter. Measurements are automatically detected and captured intothe integrated reporting system. These capabilities can be combined withother vessel analysis tools to provide a comprehensive and customized EPplanning workflow for ablation and lead approach planning.

The techniques described above can also be utilized in calcium scoring.Semi-automated identification of coronary calcium is supported withAgatston, volume and mineral mass algorithms being totaled and reportedon-screen. Results may be stored in an open-format database along withvarious other data relating to the patient and their cardiovascularhistory and risk factors. A customized report can be automaticallygenerated, as part of cloud services, based upon these data. Alsoincludes report generation as defined by the Society of CardiovascularComputed Tomography (SCCT) guidelines.

The techniques described above can also be utilized in a time-volumeanalysis (TVA), which may include fully-automated calculation of leftventricular volume, ejection fraction, myocardial volume (mass) and wallthickening from multi-phasic data. A fast and efficient workflowprovided as part of cloud services allows for easy verification oradjustment of levels and contours. The results are presented within theintegrated reporting function.

The techniques described above can also be utilized in the area ofsegmentation analysis and tracking (SAT), which includes supportsanalysis and characterization of masses and structures in various scans,including pulmonary CT examinations. Features include single-clicksegmentation of masses, manual editing tools to resolve segmentationissues, automatic reporting of dimensions and volume, graphical 3Ddisplay of selected regions, integrated automated reporting tool,support for follow-up comparisons including percent volume change anddoubling time, and support for review of sphericity filter results.

The techniques described above can also be utilized in the area offlythrough which may include features of automatic segmentation andcenterline extraction of the colon, with editing tools available toredefine these centerlines if necessary. 2D review includes side-by-sidesynchronized supine and prone data sets in either axial, coronal orsagittal views with representative synchronized endoluminal views. 3Dreview includes axial, coronal and sagittal MPR or MIP image displaywith large endoluminal view and an unfolded view that displays theentire colon. Coverage tracking is supported to ensure 100% coveragewith stepwise review of unviewed sections, one-click polypidentification, bookmark and merge findings, as well as a cube view forisolating a volume of interest and an integrated contextual reportingtool. Support is provided for use of sphericity filter results.

The techniques described above can also be utilized in the area oftime-dependent analysis (TDA), which provides assessment tools foranalyzing the time-dependent behavior of appropriate computerizedtomographic angiography (CTA) and/or MRI examinations, such as withincerebral perfusion studies. Features include support for loadingmultiple time-dependent series at the same time, and a proceduralworkflow for selecting input and output function and regions ofinterest. An integrated reporting tool is provided as well as theability to export the blood flow, blood volume and transit time maps toDICOM. The tools may also be used with time-dependent MR acquisitions tocalculate various time-dependent parameters.

The techniques described above can also be utilized in the area ofCTA-CT subtraction, which includes automatic registration of pre- andpost-contrast images, followed by subtraction or dense-voxel maskingtechnique which removes high-intensity structures (like bone andsurgical clips) from the CTA scan without increasing noise, and leavingcontrast-enhanced vascular structures intact.

The techniques described above can also be utilized in dental analysis,which provides a CPR tool which can be applied for review of dental CTscans, offering the ability to generate “panoramic” projections invarious planes and of various thicknesses, and cross-sectional MPR viewsat set increments along the defined curve plane.

The techniques described above can also be utilized in the area ofmulti-phase MR (basic, e.g. breast, prostate MR). Certain MRexaminations (for example, breast, prostate MR) involve a series ofimage acquisitions taken over a period of time, where certain structuresbecome enhanced over time relative to other structures. This modulefeatures the ability to subtract a pre-enhancement image from allpost-enhancement images to emphasize visualization of enhancingstructures (for example, vascular structures and other enhancingtissue). Time-dependent region-of-interest tools are provided to plottime-intensity graphs of a given region.

The techniques described above can also be utilized in parametricmapping (e.g. for multi-phase Breast MR), in which the parametricmapping module pre-calculates overlay maps where each pixel in an imageis color-coded depending on the time-dependent behavior of the pixelintensity. The techniques described above can also be utilized in thearea of SphereFinder (e.g. sphericity filter for lung and colon).SphereFinder pre-processes datasets as soon as they are received andapplies filters to detect sphere-like structures. This is often usedwith lung or colon CT scans to identify potential areas of interest. Thetechniques described can also be utilized in fusion for CT/MR/PET/SPECT.Any two CT, PET, MR or SPECT series, or any two-series combination canbe overlaid with one assigned a semi-transparent color coding and theother shown in grayscale and volume rendering for anatomical reference.Automatic registration is provided and subtraction to a temporary seriesor to a saved, third series is possible.

The techniques described above can also be utilized in the area ofLobular Decomposition. Lobular Decomposition is an analysis andsegmentation tool that is designed with anatomical structures in mind.For any structure or organ region which is intertwined with a tree-likestructure (such as an arterial and/or venous tree), the LobularDecomposition tool allows the user to select the volume of interest, aswell as the trees related to it, and to partition the volume into lobesor territories which are most proximal to the tree or any specificsub-branch thereof. This generic and flexible tool has potentialresearch applications in analysis of the liver, lung, heart and variousother organs and pathological structures.

The techniques described above can also be utilized in the area ofVolumetric Histogram. Volumetric Histogram supports analysis of a givenvolume of interest based on partition of the constituent voxels intopopulations of different intensity or density ranges. This can be used,for example, to support research into disease processes such as cancer(where it is desirable to analyze the composition of tumors, in anattempt to understand the balance between active tumor, necrotic tissue,and edema), or emphysema (where the population of low-attenuation voxelsin a lung CT examination may be a meaningful indicator of earlydisease).

The techniques described above can also be utilized in the area ofMotion Analytics. Motion Analytics provides a powerful 2D representationof a 4D process, for more effective communication of findings wheninteractive 3D or 4D display is not available. Any dynamic volumeacquisition, such as a beating heart, can be subjected to the MotionAnalysis, to generate a color-coded “trail” of outlines of keyboundaries, throughout the dynamic sequence, allowing a single 2D frameto capture and illustrate the motion, in a manner that can be readilyreported in literature. The uniformity of the color pattern, or lackthereof, reflects the extent to which motion is harmonic, providingimmediate visual feedback from a single image.

FIGS. 11A and 11B are block diagrams illustrating a cloud-based imageprocessing system according to certain embodiments of the invention.Referring to FIG. 11A, according to one embodiment, system 1100 includesone or more entities or institutes 1101-1102 communicatively coupled tocloud 1103 over a network. Entities 1101-1102 may represent a variety oforganizations such as medical institutes having a variety of facilitiesresiding all over the world. For example, entity 1101 may include or beassociated with image capturing device or devices 1104, image storagesystem (e.g., PACS) 1105, router 1106, and/or data gateway manager 1107.Image storage system 1105 may be maintained by a third party entity thatprovides archiving services to entity 1101, which may be accessed byworkstation 1108 such as an administrator or user associated with entity1101. Note that throughout this application, a medical institute isutilized as an example of an organization entity. However, it is not solimited; other organizations or entities may also be applied.

In one embodiment, cloud 1103 may represent a set of servers or clustersof servers associated with a service provider and geographicallydistributed over a network. For example, cloud 1103 may be associatedwith a medical image processing service provider such as TeraRecon ofFoster City, Calif. A network may be a local area network (LAN), ametropolitan area network (MAN), a wide area network (WAN) such as theInternet or an intranet, or a combination thereof. Cloud 1103 can bemade of a variety of servers and devices capable of providingapplication services to a variety of clients such as clients 1113-1116over a network. In one embodiment, cloud 1103 includes one or more cloudservers 1109 to provide image processing services, one or more databases1110 to store images and other medical data, and one or more routers1112 to transfer data to/from other entities such as entities 1101-1102.If the cloud server consists of a server cluster, or more than oneserver, rules may exist which control the transfer of data between theservers in the cluster. For example, there may be reasons why data on aserver in one country should not be placed on a server in anothercountry.

Server 1109 may be an image processing server to provide medical imageprocessing services to clients 1113-1116 over a network. For example,server 1109 may be implemented as part of a TeraRecon AquariusNET™server and/or a TeraRecon AquariusAPS server. Data gateway manager 1107and/or router 1106 may be implemented as part of a TeraReconAquariusGATE device. Medical imaging device 1104 may be an imagediagnosis device, such as X-ray CT device, MRI scanning device, nuclearmedicine device, ultrasound device, or any other medical imaging device.Medical imaging device 1104 collects information from multiplecross-section views of a specimen, reconstructs them, and producesmedical image data for the multiple cross-section views. Medical imagingdevice 1104 is also referred to as a modality.

Database 1110 may be a data store to store medical data such as digitalimaging and communications in medicine (DICOM) compatible data or otherimage data. Database 1110 may also incorporate encryption capabilities.Database 1110 may include multiple databases and/or may be maintained bya third party vendor such as storage providers. Data store 1110 may beimplemented with relational database management systems (RDBMS), e.g.,Oracle™ database or Microsoft® SQL Server, etc. Clients 1113-1116 mayrepresent a variety of client devices such as a desktop, laptop, tablet,mobile phone, personal digital assistant (PDA), etc. Some of clients1113-1116 may include a client application (e.g., thin clientapplication) to access resources such as medical image processing toolsor applications hosted by server 1109 over a network. Examples of thinclients include a web browser, a phone application and others.

According to one embodiment, server 1109 is configured to provideadvanced image processing services to clients 1113-1116, which mayrepresent physicians from medical institutes, instructors, students,agents from insurance companies, patients, medical researchers, etc.Cloud server 1109, also referred to as an image processing server, hasthe capability of hosting one or more medical images and data associatedwith the medical images to allow multiple participants such as clients1113-1116, to participate in a discussion/processing forum of the imagesin a collaborated manner or conferencing environment. Differentparticipants may participate in different stages and/or levels of adiscussion session or a workflow process of the images.

According to some embodiments, data gateway manager 1107 is configuredto automatically or manually transfer medical data to/from dataproviders (e.g., PACS systems) such as medical institutes. Such datagateway management may be performed based on a set of rules or policies,which may be configured by an administrator or authorized personnel. Inone embodiment, in response to updates of medical images data during animage discussion session or image processing operations performed in thecloud, the data gateway manager is configured to transmit over a network(e.g., Internet) the updated image data or the difference between theupdated image data and the original image data to a data provider suchas PACS 1105 that provided the original medical image data. Similarly,data gateway manager 1107 can be configured to transmit any new imagesand/or image data from the data provider, where the new images may havebeen captured by an image capturing device such as image capturingdevice 1104 associated with entity 1101. In addition, data gatewaymanager 1107 may further transfer data amongst multiple data providersthat is associated with the same entity (e.g., multiple facilities of amedical institute). Furthermore, cloud 1103 may include an advancedpreprocessing system (not shown) to automatically perform certainpre-processing operations of the received images using certain advancedimage processing resources provided by the cloud systems. In oneembodiment, gateway manager 1107 is configured to communicate with cloud1103 via certain Internet ports such as port 80 or 443, etc. The databeing transferred may be encrypted and/or compressed using a variety ofencryption and compression methods. The term “Internet port” in thiscontext could also be an intranet port, or a private port such as port80 or 443 etc. on an intranet.

FIG. 12 is a block diagram of a data processing system, which may beused with one embodiment of the invention. For example, the system 1200may be used as part of a server or a client as described above. Forexample, system 1200 may represent image processing server 110, which iscommunicatively coupled to a remote client device or another server vianetwork interface 1210. Advanced image processing system 140, automaticimage processing system 150, and image processing engine 104, asdescribed above, may be hosted by system 1200. In one embodiment,advanced image processing system 140 and/or automatic image processingsystem 150 may be loaded in memory 1205 and executed by processor 1203to perform various operations or processes as described above.

Note that while FIG. 12 illustrates various components of a computersystem, it is not intended to represent any particular architecture ormanner of interconnecting the components; as such details are notgermane to the present invention. It will also be appreciated thatnetwork computers, handheld computers, cell phones and other dataprocessing systems which have fewer components or perhaps morecomponents may also be used with the present invention.

As shown in FIG. 12, the computer system 1200, which is a form of a dataprocessing system, includes a bus or interconnect 1202 which is coupledto one or more microprocessors 1203 and a ROM 1207, a volatile RAM 1205,and a non-volatile memory 1206. The microprocessor 1203 is coupled tocache memory 1204. The bus 1202 interconnects these various componentstogether and also interconnects these components 1203, 1207, 1205, and1206 to a display controller and display device 1208, as well as toinput/output (I/O) devices 1210, which may be mice, keyboards, modems,network interfaces, printers, and other devices which are well-known inthe art.

Typically, the input/output devices 1210 are coupled to the systemthrough input/output controllers 1209. The volatile RAM 1205 istypically implemented as dynamic RAM (DRAM) which requires powercontinuously in order to refresh or maintain the data in the memory. Thenon-volatile memory 1206 is typically a magnetic hard drive, a magneticoptical drive, an optical drive, or a DVD RAM or other type of memorysystem which maintains data even after power is removed from the system.Typically, the non-volatile memory will also be a random access memory,although this is not required.

While FIG. 12 shows that the non-volatile memory is a local devicecoupled directly to the rest of the components in the data processingsystem, the present invention may utilize a non-volatile memory which isremote from the system; such as, a network storage device which iscoupled to the data processing system through a network interface suchas a modem or Ethernet interface. The bus 1202 may include one or morebuses connected to each other through various bridges, controllers,and/or adapters, as is well-known in the art. In one embodiment, the I/Ocontroller 1209 includes a USB (Universal Serial Bus) adapter forcontrolling USB peripherals. Alternatively, I/O controller 1209 mayinclude an IEEE-1394 adapter, also known as FireWire adapter, forcontrolling FireWire devices.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The techniques shown in the figures can be implemented using code anddata stored and executed on one or more electronic devices. Suchelectronic devices store and communicate (internally and/or with otherelectronic devices over a network) code and data using computer-readablemedia, such as non-transitory computer-readable storage media (e.g.,magnetic disks; optical disks; random access memory; read only memory;flash memory devices; phase-change memory) and transitorycomputer-readable transmission media (e.g., electrical, optical,acoustical or other form of propagated signals—such as carrier waves,infrared signals, digital signals).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), firmware, software (e.g., embodied on anon-transitory computer readable medium), or a combination of both.Although the processes or methods are described above in terms of somesequential operations, it should be appreciated that some of theoperations described may be performed in a different order. Moreover,some operations may be performed in parallel rather than sequentially.

In the foregoing specification, embodiments of the invention have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the invention as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. An image processing system, comprising: aprocessor; an image storage system that stores medical data includingmedical images used in medical diagnoses; an image processing engine,the image processing engine processing the medical images in response toimage processing commands, the processing on the medical imagesincluding changing image processing parameters used to display themedical images, the parameters including parameters that specify updatedviews of the medical images; an advanced image processing system thatallows the user to directly input desired values of the image processingparameters, the advance image processing system receiving input from theuser indicating the desired values and the advanced image processingsystem generating commands that are sent to the image processing enginecausing the image process engine to change the image processingparameters to match the desired values of the image processingparameters to produce updated medical images; and, an automatic imageprocessing system that processes a series of medical images to obtainpreliminary medical results, the preliminary medical results includingpreliminary result medical images based on the series of medical imagesand the preliminary results including identification of anatomicalfeatures of interest and measured values of the anatomical features ofinterest, the automatic image processing system including: a graphicaluser interface, the graphical user interface presenting the user with aseries of interactive questions pertaining to the anatomical features ofinterest and measured values of the anatomical features of interestwithin the preliminary medical results, answers from the user being usedby the automatic image processing system to adjust selection ormeasurement of the anatomical features of interest within thepreliminary result medical images to produce the updated medical images;wherein commands sent based on calculated values of the image processingparameters are sent to the image processing engine directly by theautomatic image processing system or through the advanced imageprocessing system.
 2. An image processing system as in claim 1,additionally comprising: a display by which the updated medical imagesare displayed to the user; wherein the automatic image processing systemprompts the user to determine whether the user is satisfied with anupdated medical image, and if the user is not satisfied with the updatedmedical image, the graphical user interface presents the user with oneor more additional interactive questions to additionally obtainadditional information about the user preferences, the additionalinformation being used by the automatic image processing system togenerate additional calculated values of the image processing parametersso that commands are sent to the image processing engine causing theimage process engine to change the image processing parameters to matchthe additional calculated values of the image processing parameters toproduce another updated medical image.
 3. An image processing system asin claim 1, additionally comprising: a workflow management system thatmanages creation, update and deletion of workflow templates, theworkflow management system capturing workflows that are a repetitivepattern of activities used in a process for generating medical imageviews for diagnosis, the workflows modeling and documenting medicalimage processing practices, the workflows being for specific medicalimage studies being modeled by the workflow templates, wherein aworkflow template is a template with a predefined set of workflow stagesforming a logical workflow.
 4. An image processing system as in claim 1,wherein the automatic image processing system operates as an imageprocessing wizard that guides the user through an advanced imageprocessing process automating steps based on obtained or assumed userpreferences.
 5. An image processing system as in claim 1, additionallycomprising: a workflow management system that manages creation, updateand deletion of workflow templates, the workflow management systemcapturing workflows that are a repetitive pattern of activities used ina process for generating medical image views for diagnosis, theworkflows modeling and documenting medical image processing practices,the workflows being for specific medical image studies being modeled bythe workflow templates.
 6. An image processing system as in claim 1,additionally comprising: a workflow management system that managescreation, update and deletion of workflow templates, the workflowmanagement system capturing workflows that are a repetitive pattern ofactivities used in a process for generating medical image views fordiagnosis, the workflows modeling and documenting medical imageprocessing practices, the workflows being for specific medical imagestudies being modeled by the workflow templates; wherein a workflowtemplate is a template with a predefined set of workflow stages forminga logical workflow; and wherein workflow stages in a workflow templateare ordered sequentially, with lower order stages being performed beforehigher order stages.
 7. A method as in claim 1, additionally comprising:a workflow management system that manages creation, update and deletionof workflow templates, the workflow management system capturingworkflows that are a repetitive pattern of activities used in a processfor generating medical image views for diagnosis, the workflows modelingand documenting medical image processing practices, the workflows beingfor specific medical image studies being modeled by the workflowtemplates; wherein a workflow template is a template with a predefinedset of workflow stages forming a logical workflow; and whereindependency relationships are maintained among workflow stages so that aworkflow stage dependent upon other workflow stages is not performeduntil performance of the other workflow stages is complete.
 8. An imageprocessing system as in claim 1, wherein the image processing enginechanges the image processing parameters using metadata that allowsoriginal medical images to be recovered from updated medical images. 9.An image processing system as in claim 1, wherein the automatic imageprocessing system includes an automated processing module that receivespatient information and procedure information and uses the patientinformation and the procedure information to generate a series ofmedical images from the medical data stored in the image storage system,the series of medical images showing several medical images withdifferent views of anatomy within a patient.
 10. A method for processingand using medical images, the method comprising: storing medical dataincluding medical images used in medical diagnoses within an imagestorage system; processing, by an automatic image processing system, aseries of medical images to obtain preliminary medical results, thepreliminary medical results including preliminary result medical imagesbased on the series of medical images and the preliminary resultsincluding identification of anatomical features of interest and measuredvalues of the anatomical features of interest; providing a graphicaluser interface by the automatic image processing system, the graphicaluser interface presenting the user with a series of interactivequestions pertaining to the anatomical features of interest and measuredvalues of the anatomical features of interest within the preliminarymedical results, answers from the user being used by the automatic imageprocessing system to adjust selection or measurement of the anatomicalfeatures of interest within the preliminary result medical images toproduce the updated medical images; and providing an image processingengine that in response to image processing commands, processes themedical images including changing image processing parameters used todisplay the medical images, the parameters including parameters thatspecify updated views of the medical images including: providing anadvanced image processing system that generates commands that are sentto the image processing engine causing the image process engine tochange the image processing parameters to match the desired values ofimage processing parameters; wherein the commands sent based on thecalculated values of the image processing parameters are sent to theimage processing engine directly by the automatic image processingsystem or through the advanced image processing system.
 11. A method asin claim 10, additionally comprising: displaying the updated medicalimages to the user on a display; prompting the user, by the automaticimage processing system, to determine whether the user is satisfied withthe updated medical image, and if the user is not satisfied with theupdated medical image, performing the following: presenting the user, bythe graphical user interface, with one or more additional interactivequestions to additionally obtain additional information about the userpreferences, the additional information being used by the automaticimage processing system to generate additional calculated values of theimage processing parameters so that commands are sent to the imageprocessing engine causing the image process engine to change the imageprocessing parameters to match the additional calculated values of theimage processing parameters to produce another updated medical image.12. A method as in claim 10, additionally comprising: managing creation,update and deletion of workflow templates by a workflow managementsystem, the workflow management system capturing workflows that are arepetitive pattern of activities used in a process for generatingmedical image views for diagnosis, the workflows modeling anddocumenting medical image processing practices, the workflows being forspecific medical image studies being modeled by the workflow templates,wherein a workflow template is a template with a predefined set ofworkflow stages forming a logical workflow.
 13. A method as in claim 10,additionally comprising: operating the automatic image processing systemas an image processing wizard that guides the user through an advancedimage processing process automating steps based on obtained or assumeduser preferences.
 14. A method as in claim 10, additionally comprising:managing creation, update and deletion of workflow templates by aworkflow management system, the workflow management system capturingworkflows that are a repetitive pattern of activities used in a processfor generating medical image views for diagnosis, the workflows modelingand documenting medical image processing practices, the workflows beingfor specific medical image studies being modeled by the workflowtemplates; wherein a workflow template is a template with a predefinedset of workflow stages forming a logical workflow; and wherein workflowstages in a workflow template are ordered sequentially, with lower orderstages being performed before higher order stages.
 15. A method as inclaim 10, additionally comprising: managing creation, update anddeletion of workflow templates by a workflow management system, theworkflow management system capturing workflows that are a repetitivepattern of activities used in a process for generating medical imageviews for diagnosis, the workflows modeling and documenting medicalimage processing practices, the workflows being for specific medicalimage studies being modeled by the workflow templates; wherein aworkflow template is a template with a predefined set of workflow stagesforming a logical workflow; and wherein dependency relationships aremaintained among workflow stages so that a workflow stage dependent uponother workflow stages is not performed until performance of the otherworkflow stages is complete.
 16. A method as in claim 10, additionallycomprising: changing, by the image processing engine, the imageprocessing parameters using metadata that allows original medical imagesto be recovered from updated medical images.
 17. A method as in claim10, wherein the automatic image processing system includes an automatedprocessing module that receives patient information and procedureinformation and uses the patient information and the procedureinformation to generate a series of medical images from the medical datastored in the image storage system, the series of medical images showingseveral medical images with different views of anatomy within a patient.18. A medical system, comprising: a processor; a medical imaging devicethat collects information from multiple cross-section view of aspecimen; an image storage system that stores medical data includingmedical images used in medical diagnoses; an image processing engine,the image processing engine processing the medical images in response toimage processing commands, the processing on the medical imagesincluding changing image processing parameters used to display themedical images, the parameters including parameters that specify updatedviews of the medical images; an advanced image processing system thatallows the user to directly input desired values of the image processingparameters, the advance image processing system receiving input from theuser indicating the desired values and the advanced image processingsystem generating commands that are sent to the image processing enginecausing the image process engine to change the image processingparameters to match the desired values of the image processingparameters to produce updated medical images; and, an automatic imageprocessing system that processes a series of medical images to obtainpreliminary medical results, the preliminary medical results includingpreliminary result medical images based on the series of medical imagesand the preliminary results including identification of anatomicalfeatures of interest and measured values of the anatomical features ofinterest, the automatic image processing system including: a graphicaluser interface, the graphical user interface presenting the user with aseries of interactive questions pertaining to the anatomical features ofinterest and measured values of the anatomical features of interestwithin the preliminary medical results, answers from the user being usedby the automatic image processing system to adjust selection ormeasurement of the anatomical features of interest within thepreliminary result medical images to produce the updated medical images;wherein commands sent based on calculated values of the image processingparameters are sent to the image processing engine directly by theautomatic image processing system or through the advanced imageprocessing system.
 19. A medical system as in claim 18, additionallycomprising: a display by which the updated medical images are displayedto the user; wherein the automatic image processing system prompts theuser to determine whether the user is satisfied with the updated medicalimage, and if the user is not satisfied with the updated medical image,the graphical user interface presents the user with one or moreadditional interactive questions to additionally obtain additionalinformation about the user preferences, the additional information beingused by the automatic image processing system to generate additionalcalculated values of the image processing parameters so that commandsare sent to the image processing engine causing the image process engineto change the image processing parameters to match the additionalcalculated values of the image processing parameters to produce anotherupdated medical image.
 20. A medical system as in claim 18, additionallycomprising: a workflow management system that manages creation, updateand deletion of workflow templates, the workflow management systemcapturing workflows that are a repetitive pattern of activities used ina process for generating medical image views for diagnosis, theworkflows modeling and documenting medical image processing practices,the workflows being for specific medical image studies being modeled bythe workflow templates, wherein a workflow template is a template with apredefined set of workflow stages forming a logical workflow.